def test_nn_set_training_input(self):
builder = self.create_base_builder()
builder.set_mean_squared_error_loss(name='mse',
input='output',
target='target')
builder.set_adam_optimizer(
AdamParams(lr=1e-2, batch=10, beta1=0.9, beta2=0.999, eps=1e-8))
builder.set_epochs(20, allowed_set=[10, 20, 30])
builder.set_training_input([('input', datatypes.Array(3)),
('target', 'Double')])
model_path = os.path.join(self.model_dir, 'updatable_creation.mlmodel')
print(model_path)
save_spec(builder.spec, model_path)
mlmodel = MLModel(model_path)
self.assertTrue(mlmodel is not None)
spec = mlmodel.get_spec()
self.assertEqual(spec.description.trainingInput[0].name, 'input')
self.assertEqual(
spec.description.trainingInput[0].type.WhichOneof('Type'),
'multiArrayType')
self.assertEqual(spec.description.trainingInput[1].name, 'target')
self.assertEqual(
spec.description.trainingInput[1].type.WhichOneof('Type'),
'doubleType')
def test_nn_set_training_input(self):
builder = self.create_base_builder()
builder.set_mean_squared_error_loss(name="mse",
input_feature=("output",
datatypes.Array(3)))
builder.set_adam_optimizer(
AdamParams(lr=1e-2, batch=10, beta1=0.9, beta2=0.999, eps=1e-8))
builder.set_epochs(20, allowed_set=[10, 20, 30])
model_path = os.path.join(self.model_dir, "updatable_creation.mlmodel")
print(model_path)
save_spec(builder.spec, model_path)
mlmodel = MLModel(model_path)
self.assertTrue(mlmodel is not None)
spec = mlmodel.get_spec()
self.assertEqual(spec.description.trainingInput[0].name, "input")
self.assertEqual(
spec.description.trainingInput[0].type.WhichOneof("Type"),
"multiArrayType")
self.assertEqual(spec.description.trainingInput[1].name, "output_true")
self.assertEqual(
spec.description.trainingInput[1].type.WhichOneof("Type"),
"multiArrayType")
def _keras_2_mlmodel_image():
"""
Converts a Keras h5 model into ML Model for image data and saves it on
disk.
NOTE: Image configuration must be specified from Explora.
NOTE: Currently, only categorical cross entropy loss is supported.
"""
model = get_keras_model()
ios_config = state.state["ios_config"]
class_labels = ios_config["class_labels"]
mlmodel = keras_converter.convert(model, input_names=['image'],
output_names=['output'],
class_labels=class_labels,
predicted_feature_name='label')
mlmodel.save(state.state["mlmodel_path"])
image_config = ios_config["image_config"]
spec = coremltools.utils.load_spec(state.state["mlmodel_path"])
builder = coremltools.models.neural_network.NeuralNetworkBuilder(spec=spec)
dims = image_config["dims"]
spec.description.input[0].type.imageType.width = dims[0]
spec.description.input[0].type.imageType.height = dims[1]
cs = _FeatureTypes_pb2.ImageFeatureType.ColorSpace.Value(image_config["color_space"])
spec.description.input[0].type.imageType.colorSpace = cs
trainable_layer_names = [layer.name for layer in model.layers if layer.get_weights()]
builder.make_updatable(trainable_layer_names)
builder.set_categorical_cross_entropy_loss(name='loss', input='output')
if isinstance(model.optimizer, SGD):
params = SgdParams(
lr=K.eval(model.optimizer.lr),
batch=state.state["hyperparams"]["batch_size"],
)
builder.set_sgd_optimizer(params)
elif isinstance(model.optimizer, Adam):
params = AdamParams(
lr=K.eval(model.optimizer.lr),
batch_size=state.state["hyperparams"]["batch_size"],
beta1=model.optimizer.beta1,
beta2=model.optimizer.beta2,
eps=model.optimizer.eps,
)
builder.set_adam_optimizer(params)
else:
raise Exception("iOS optimizer must be SGD or Adam!")
builder.set_epochs(UNLIMITED_EPOCHS)
builder.set_shuffle(state.state["hyperparams"]["shuffle"])
mlmodel_updatable = MLModel(spec)
mlmodel_updatable.save(state.state["mlmodel_path"])
K.clear_session()
def test_nn_builder_with_training_features(self):
input_features = [('input', datatypes.Array(3))]
output_features = [('output', None)]
training_features = [('input', datatypes.Array(3)),
('target', datatypes.Double)]
builder = NeuralNetworkBuilder(input_features,
output_features,
disable_rank5_shape_mapping=True,
training_features=training_features)
W1 = _np.random.uniform(-0.5, 0.5, (3, 3))
W2 = _np.random.uniform(-0.5, 0.5, (3, 3))
builder.add_inner_product(name='ip1',
W=W1,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name='input',
output_name='hidden')
builder.add_inner_product(name='ip2',
W=W2,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name='hidden',
output_name='output')
builder.make_updatable(['ip1', 'ip2']) # or a dict for weightParams
builder.set_mean_squared_error_loss(name='mse',
input='output',
target='target')
builder.set_adam_optimizer(
AdamParams(lr=1e-2, batch=10, beta1=0.9, beta2=0.999, eps=1e-8))
builder.set_epochs(20, allowed_set=[10, 20, 30])
builder.set_training_input([('input', datatypes.Array(3)),
('target', 'Double')])
model_path = os.path.join(self.model_dir, 'updatable_creation.mlmodel')
print(model_path)
save_spec(builder.spec, model_path)
mlmodel = MLModel(model_path)
self.assertTrue(mlmodel is not None)
spec = mlmodel.get_spec()
self.assertEqual(spec.description.trainingInput[0].name, 'input')
self.assertEqual(
spec.description.trainingInput[0].type.WhichOneof('Type'),
'multiArrayType')
self.assertEqual(spec.description.trainingInput[1].name, 'target')
self.assertEqual(
spec.description.trainingInput[1].type.WhichOneof('Type'),
'doubleType')
def test_nn_builder_with_training_features(self):
input_features = [("input", datatypes.Array(3))]
output_features = [("output", datatypes.Array(3))]
builder = NeuralNetworkBuilder(input_features, output_features)
W1 = _np.random.uniform(-0.5, 0.5, (3, 3))
W2 = _np.random.uniform(-0.5, 0.5, (3, 3))
builder.add_inner_product(
name="ip1",
W=W1,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name="input",
output_name="hidden",
)
builder.add_inner_product(
name="ip2",
W=W2,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name="hidden",
output_name="output",
)
builder.make_updatable(["ip1", "ip2"]) # or a dict for weightParams
builder.set_mean_squared_error_loss(name="mse",
input_feature=("output",
datatypes.Array(3)))
builder.set_adam_optimizer(
AdamParams(lr=1e-2, batch=10, beta1=0.9, beta2=0.999, eps=1e-8))
builder.set_epochs(20, allowed_set=[10, 20, 30])
model_path = os.path.join(self.model_dir, "updatable_creation.mlmodel")
print(model_path)
save_spec(builder.spec, model_path)
mlmodel = MLModel(model_path)
self.assertTrue(mlmodel is not None)
spec = mlmodel.get_spec()
self.assertEqual(spec.description.trainingInput[0].name, "input")
self.assertEqual(
spec.description.trainingInput[0].type.WhichOneof("Type"),
"multiArrayType")
self.assertEqual(spec.description.trainingInput[1].name, "output_true")
self.assertEqual(
spec.description.trainingInput[1].type.WhichOneof("Type"),
"multiArrayType")
def test_updatable_model_creation_mse_adam(self):
builder = self.create_base_builder()
builder.set_mean_squared_error_loss(name="mse",
input_feature=("output",
datatypes.Array(3)))
builder.set_adam_optimizer(
AdamParams(lr=1e-2, batch=10, beta1=0.9, beta2=0.999, eps=1e-8))
builder.set_epochs(20, allowed_set=[10, 20, 30])
model_path = os.path.join(self.model_dir, "updatable_creation.mlmodel")
print(model_path)
save_spec(builder.spec, model_path)
mlmodel = MLModel(model_path)
self.assertTrue(mlmodel is not None)
spec = mlmodel.get_spec()
self.assertTrue(spec.isUpdatable)
self.assertTrue(spec.neuralNetwork.layers[0].isUpdatable)
self.assertTrue(
spec.neuralNetwork.layers[0].innerProduct.weights.isUpdatable)
self.assertTrue(spec.neuralNetwork.layers[1].isUpdatable)
self.assertTrue(
spec.neuralNetwork.layers[1].innerProduct.weights.isUpdatable)
self.assertTrue(spec.neuralNetwork.updateParams.lossLayers[0].
categoricalCrossEntropyLossLayer is not None)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.adamOptimizer
is not None)
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.
learningRate.defaultValue,
1e-2,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.
miniBatchSize.defaultValue,
10,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.beta1.
defaultValue,
0.9,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.beta2.
defaultValue,
0.999,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.eps.
defaultValue,
1e-8,
atol=1e-8,
))
self.assertTrue(
_np.isclose(spec.neuralNetwork.updateParams.epochs.defaultValue,
20,
atol=1e-4))
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.learningRate.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.learningRate.range.maxValue == 1)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.miniBatchSize.set.values == [10])
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.beta1.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.beta1.range.maxValue == 1)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.beta2.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.beta2.range.maxValue == 1)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.eps.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.eps.range.maxValue == 1)
self.assertTrue(
spec.neuralNetwork.updateParams.epochs.set.values == [10, 20, 30])
def test_updatable_model_creation_ce_adam(self):
builder = self.create_base_builder()
builder.add_softmax(name="softmax",
input_name="output",
output_name="softmax_output")
builder.set_categorical_cross_entropy_loss(name="cross_entropy",
input="softmax_output")
adam_params = AdamParams()
adam_params.set_batch(value=10, allowed_set=[10, 20])
builder.set_adam_optimizer(adam_params)
builder.set_epochs(20)
model_path = os.path.join(self.model_dir, "updatable_creation.mlmodel")
print(model_path)
save_spec(builder.spec, model_path)
mlmodel = MLModel(model_path)
self.assertTrue(mlmodel is not None)
spec = mlmodel.get_spec()
self.assertTrue(spec.isUpdatable)
self.assertTrue(spec.neuralNetwork.layers[0].isUpdatable)
self.assertTrue(
spec.neuralNetwork.layers[0].innerProduct.weights.isUpdatable)
self.assertTrue(spec.neuralNetwork.layers[1].isUpdatable)
self.assertTrue(
spec.neuralNetwork.layers[1].innerProduct.weights.isUpdatable)
self.assertTrue(spec.neuralNetwork.updateParams.lossLayers[0].
categoricalCrossEntropyLossLayer is not None)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.adamOptimizer
is not None)
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.
learningRate.defaultValue,
1e-2,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.
miniBatchSize.defaultValue,
10,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.beta1.
defaultValue,
0.9,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.beta2.
defaultValue,
0.999,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.adamOptimizer.eps.
defaultValue,
1e-8,
atol=1e-8,
))
self.assertTrue(
_np.isclose(spec.neuralNetwork.updateParams.epochs.defaultValue,
20,
atol=1e-4))
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.learningRate.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.learningRate.range.maxValue == 1)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.miniBatchSize.set.values == [10, 20])
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.beta1.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.beta1.range.maxValue == 1)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.beta2.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.beta2.range.maxValue == 1)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.eps.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.
adamOptimizer.eps.range.maxValue == 1)
self.assertTrue(
spec.neuralNetwork.updateParams.epochs.set.values == [20])
layer.innerProduct.weights.floatValue.extend(weights)
layer.innerProduct.bias.floatValue.extend(biases)
spec.neuralNetworkClassifier.stringClassLabels.vector.extend(labels)
# Make this model trainable.
builder = coremltools.models.neural_network.NeuralNetworkBuilder(
spec=model._spec)
builder.make_updatable(["fullyconnected0"])
builder.set_categorical_cross_entropy_loss(name="lossLayer",
input="labelProbability")
builder.set_adam_optimizer(
AdamParams(lr=0.01, batch=16, beta1=0.9, beta2=0.999, eps=1e-8))
builder.set_epochs(10)
builder.spec.description.trainingInput[0].shortDescription = "Example image"
builder.spec.description.trainingInput[1].shortDescription = "True label"
coremltools.utils.save_spec(builder.spec, "../Models/HandsTuri.mlmodel")
# Replace the weights of the last layer with random weights.
model = coremltools.models.MLModel("../Models/HandsTuri.mlmodel")
model.short_description = ""
# The very last layer is softmax, we need the one before that
spec = model._spec
layer = spec.neuralNetworkClassifier.layers[-2]